{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### IO操作(读取本地数据文件)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "us_file_path = \"./youtube_video_data/US_video_data_numbers.csv\"\n",
    "uk_file_path = './youtube_video_data/GB_video_data_numbers.csv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[4.394029e+06, 3.200530e+05, 5.931000e+03, 4.624500e+04],\n",
       "       [7.860119e+06, 1.858530e+05, 2.667900e+04, 0.000000e+00],\n",
       "       [5.845909e+06, 5.765970e+05, 3.977400e+04, 1.707080e+05],\n",
       "       ...,\n",
       "       [1.424630e+05, 4.231000e+03, 1.480000e+02, 2.790000e+02],\n",
       "       [2.162240e+06, 4.103200e+04, 1.384000e+03, 4.737000e+03],\n",
       "       [5.150000e+05, 3.472700e+04, 1.950000e+02, 4.722000e+03]])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t1 = np.loadtxt(us_file_path, delimiter=',')\n",
    "t1"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "科学计数法"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[4394029,  320053,    5931,   46245],\n",
       "       [7860119,  185853,   26679,       0],\n",
       "       [5845909,  576597,   39774,  170708],\n",
       "       ...,\n",
       "       [ 142463,    4231,     148,     279],\n",
       "       [2162240,   41032,    1384,    4737],\n",
       "       [ 515000,   34727,     195,    4722]])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t1 = np.loadtxt(us_file_path, delimiter=',', dtype='int')\n",
    "t1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[4394029, 7860119, 5845909, ...,  142463, 2162240,  515000],\n",
       "       [ 320053,  185853,  576597, ...,    4231,   41032,   34727],\n",
       "       [   5931,   26679,   39774, ...,     148,    1384,     195],\n",
       "       [  46245,       0,  170708, ...,     279,    4737,    4722]])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t2 = np.loadtxt(us_file_path, delimiter=',', dtype='int', unpack=True)\n",
    "t2"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "转置"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 索引和切片"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 取行"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[4394029,  320053,    5931,   46245],\n",
       "       [7860119,  185853,   26679,       0],\n",
       "       [5845909,  576597,   39774,  170708],\n",
       "       ...,\n",
       "       [ 142463,    4231,     148,     279],\n",
       "       [2162240,   41032,    1384,    4737],\n",
       "       [ 515000,   34727,     195,    4722]])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5845909,  576597,   39774,  170708])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 取行\n",
    "t1[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[5845909,  576597,   39774,  170708],\n",
       "       [2642103,   24975,    4542,   12829],\n",
       "       [1168130,   96666,     568,    6666],\n",
       "       ...,\n",
       "       [ 142463,    4231,     148,     279],\n",
       "       [2162240,   41032,    1384,    4737],\n",
       "       [ 515000,   34727,     195,    4722]])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 取连续的多行\n",
    "t1[2:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "ename": "IndexError",
     "evalue": "index 4 is out of bounds for axis 1 with size 4",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-9-30abd842c85f>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m# 取不连续的多行\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mt1\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m4\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mIndexError\u001b[0m: index 4 is out of bounds for axis 1 with size 4"
     ]
    }
   ],
   "source": [
    "# 取不连续的多行\n",
    "t1[1,4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[7860119,  185853,   26679,       0],\n",
       "       [1168130,   96666,     568,    6666]])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 取不连续的多行\n",
    "t1[[1,4]] #第2行和第5行"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 取列\n",
    "\n",
    "#### 取整列\n",
    "\n",
    "如果切片只有冒号，表示选取该方向的整个轴"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[4394029, 7860119, 5845909, ...,  142463, 2162240,  515000],\n",
       "       [ 320053,  185853,  576597, ...,    4231,   41032,   34727],\n",
       "       [   5931,   26679,   39774, ...,     148,    1384,     195],\n",
       "       [  46245,       0,  170708, ...,     279,    4737,    4722]])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([320053, 185853, 576597, ...,   4231,  41032,  34727])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 取一行\n",
    "t2[1,:] #等价于 t2[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  5931,  26679,  39774, ...,    148,   1384,    195],\n",
       "       [ 46245,      0, 170708, ...,    279,   4737,   4722]])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 取连续的行\n",
    "t2[2:,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "ename": "IndexError",
     "evalue": "too many indices for array: array is 2-dimensional, but 3 were indexed",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-14-1a7b182d8167>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mt2\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mIndexError\u001b[0m: too many indices for array: array is 2-dimensional, but 3 were indexed"
     ]
    }
   ],
   "source": [
    "t2[1,0,3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 320053,  185853,  576597, ...,    4231,   41032,   34727],\n",
       "       [4394029, 7860119, 5845909, ...,  142463, 2162240,  515000],\n",
       "       [  46245,       0,  170708, ...,     279,    4737,    4722]])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 取不连续的行\n",
    "t2[[1,0,3],:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4394029,  320053,    5931,   46245])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 取第一列\n",
    "t2[:,0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[4394029, 7860119, 5845909, ...,  142463, 2162240,  515000],\n",
       "       [ 320053,  185853,  576597, ...,    4231,   41032,   34727],\n",
       "       [   5931,   26679,   39774, ...,     148,    1384,     195],\n",
       "       [  46245,       0,  170708, ...,     279,    4737,    4722]])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[5845909, 2642103, 1168130, ...,  142463, 2162240,  515000],\n",
       "       [ 576597,   24975,   96666, ...,    4231,   41032,   34727],\n",
       "       [  39774,    4542,     568, ...,     148,    1384,     195],\n",
       "       [ 170708,   12829,    6666, ...,     279,    4737,    4722]])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 取连续的多列\n",
    "t2[:,2:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[5845909, 1168130],\n",
       "       [ 576597,   96666],\n",
       "       [  39774,     568],\n",
       "       [ 170708,    6666]])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 取不连续的多列\n",
    "t2[:,[2,4]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4542"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 取值\n",
    "a = t2[2,3]\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.int32"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[5845909, 2642103],\n",
       "       [ 576597,   24975]])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 取连续行和连续列\n",
    "t2[0:2,2:4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4394029,   26679,    4542])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 取多个不相邻的点\n",
    "t2[[0,2,2],[0,1,3]] #3个点,坐标为: (0,0), (2,1), (2,3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2,  3,  4,  5],\n",
       "       [ 6,  7,  8,  9, 10, 11],\n",
       "       [12, 13, 14, 15, 16, 17],\n",
       "       [18, 19, 20, 21, 22, 23]])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t = np.arange(24).reshape((4,6))\n",
    "t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ True,  True,  True,  True,  True,  True],\n",
       "       [ True,  True,  True,  True, False, False],\n",
       "       [False, False, False, False, False, False],\n",
       "       [False, False, False, False, False, False]])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t<10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 3,  3,  3,  3,  3,  3],\n",
       "       [ 3,  3,  3,  3, 10, 11],\n",
       "       [12, 13, 14, 15, 16, 17],\n",
       "       [18, 19, 20, 21, 22, 23]])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t[t<10] = 3\n",
    "t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([21, 22, 23])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t[t>20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3,)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t[t>20].shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> 可见, 使用布尔索引后, 返回的naarray对象形状改变了 !"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 三元运算符\n",
    "\n",
    "`where`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = 3 if 3>2 else 4\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = 3 if 3<2 else 4\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "t = np.arange(24).reshape((4,6))\n",
    "t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.where(t<=3,100,300)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### clp 截尾操作"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.,  1.,  2.,  3.,  4.,  5.],\n",
       "       [ 6.,  7.,  8.,  9., 10., 11.],\n",
       "       [12., 13., 14., 15., 16., 17.],\n",
       "       [18., 19., 20., 21., 22., 23.]])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t3 = np.arange(24,dtype=np.float64).reshape((4,6)) #必须是浮点数类型才可以转换成 np.nan\n",
    "t3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.,  1.,  2.,  3.,  4.,  5.],\n",
       "       [ 6.,  7.,  8.,  9., 10., 11.],\n",
       "       [12., 13., 14., 15., 16., 17.],\n",
       "       [18., 19., 20., nan, nan, nan]])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t3[t3>20] = np.nan\n",
    "t3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[10., 10., 10., 10., 10., 10.],\n",
       "       [10., 10., 10., 10., 10., 11.],\n",
       "       [12., 13., 14., 15., 16., 17.],\n",
       "       [18., 18., 18., nan, nan, nan]])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t3.clip(10,18)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "小于10的替换为10，大于18的替换为了18，但是nan没有被替换，那么nan是什么？"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### numpy 中的 nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.,  1.,  2.,  3.],\n",
       "       [ 4.,  5.,  6.,  7.],\n",
       "       [ 8.,  9., 10., 11.]], dtype=float32)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t1 = np.arange(12, dtype=np.float32).reshape((3,4))\n",
    "t1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.,  1.,  2.,  3.],\n",
       "       [ 4.,  5., nan, nan],\n",
       "       [ 8.,  9., 10., 11.]], dtype=float32)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t1[1,2:] = np.nan\n",
    "t1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[False, False, False, False],\n",
       "       [False, False,  True,  True],\n",
       "       [False, False, False, False]])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.isnan(t1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.,  1.,  2.,  3.,  4.,  5.,  8.,  9., 10., 11.], dtype=float32)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t1[t1 == t1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fill_ndarray(t1):\n",
    "    for i in range(t1.shape[1]): #遍历每一列\n",
    "        temp_col = t1[:,i] #当前的一列\n",
    "        nan_num = np.count_nonzero(temp_col != temp_col)\n",
    "        if nan_num: #不为0, 说明当前列中有nan\n",
    "            temp_not_nan_col = temp_col[temp_col == temp_col] #选出当前列中不为0的元素\n",
    "            temp_col[np.isnan(temp_col)] = temp_not_nan_col.mean() #计算均值\n",
    "    return t1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.  1.  2.  3.]\n",
      " [ 4.  5. nan nan]\n",
      " [ 8.  9. 10. 11.]]\n",
      "[[ 0.  1.  2.  3.]\n",
      " [ 4.  5.  6.  7.]\n",
      " [ 8.  9. 10. 11.]]\n"
     ]
    }
   ],
   "source": [
    "if __name__ == '__main__':\n",
    "    t1 = np.arange(12, dtype=np.float32).reshape((3,4))\n",
    "    t1[1,2:] = np.nan\n",
    "    print(t1)\n",
    "    t1 = fill_ndarray(t1)\n",
    "    print(t1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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